Analysis of breathing air flow patterns in thermal imaging.

We introduce a novel methodology to characterize breathing patterns based on thermal infrared imaging. We have retrofitted a Mid-Wave Infra-Red (MWIR) imaging system with a narrow band-pass filter in the CO(2) absorption band (4130 - 4427 nm). We use this system to record the radiation information from within the breathing flow region. Based on this information we compute the mean dynamic thermal signal of breath. The breath signal is quasi-periodic due to the interleaving of high and low intensities corresponding to expirations and inspirations respectively. We sample the signal at a constant rate and then filter the high frequency noise due to tracking instability. We detect the breathing cycles through zero cross thresholding, which is insensitive to noise around the zero line. We normalize the breathing cycles and align them at the transition point from inhalation to exhalation. Then, we compute the mean breathing cycle. We use the first eight (8) harmonic components of the mean cycle to characterize the breathing pattern. The harmonic analysis highlights the intra-individual similarity of breathing patterns. Our method opens the way for desktop, unobtrusive monitoring of human respiration and may find widespread applications in clinical studies of chronic ailments. It also brings up the intriguing possibility of using breathing patterns as a novel biometric.